Smaller LLMs Aren't Always Cheaper: Review and Rework Costs Blow Up the Bill

zeuslac · reddit · 2026-09-21

An analysis of LLM workflow economics arguing that a cheaper-per-call model can make the whole workflow more expensive, and most AI business cases never measure where it happens.

The pattern: on easy cases a small model saves money; on hard cases its suggestions are weak enough that reviewers check everything against source documents and redo the work—taking longer than before AI existed. Errors that slip through cost far more to fix later than inference ever cost.
The model bill drops while the cost of work rises, and nobody notices: the bill is the only tracked number, review time lives in another system, and corrections are rarely linked back to the task.
Two corollaries: difficulty-based routing's value depends entirely on the price gap—one price drop can erase the reason for the routing layer; and a router can be financially sound yet fail on quality, pushing overall error rates above the manual baseline while the spreadsheet shows savings.
The fix: one record per task tying model usage, review time and later corrections together, so you measure the full cost of a completed task, not the cost of a call.

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Smaller LLMs Aren't Always Cheaper: Review and Rework Costs Blow Up the Bill

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